THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Please switch to tf.contrib.estimator.*_head.

Multi-label classification handles the case where each example may have zero
or more associated labels, from a discrete set. This is distinct from
multi_class_head which has exactly one label from a discrete set.

This head by default uses sigmoid cross entropy loss, which expects as input
a multi-hot tensor of shape (batch_size, num_classes).

Args:

n_classes: Integer, number of classes, must be >= 2

label_name: String, name of the key in label dict. Can be null if label
is a tensor (single headed models).

weight_column_name: A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.

enable_centered_bias: A bool. If True, estimator will learn a centered
bias variable for each class. Rest of the model structure learns the
residual after centered bias.

head_name: name of the head. If provided, predictions, summary and metrics
keys will be suffixed by "/" + head_name and the default variable scope
will be head_name.

thresholds: thresholds for eval metrics, defaults to [.5]

metric_class_ids: List of class IDs for which we should report per-class
metrics. Must all be in the range [0, n_classes).

loss_fn: Optional function that takes (labels, logits, weights) as
parameter and returns a weighted scalar loss. weights should be
optional. See tf.losses